AUTISM

Why the Rise in Autism over the last 20 years?

Why the Rise in Autism over the last 20 years?

Introduction

The prevalence of autism has increased in many countries across the last two decades1-9 yet the reasons for the increase remain highly controversial. Whereas a large part of the increase remains unexplained, four main hypotheses have been supported by empirical data as contributing to the increase in prevalence: (i) diagnostic definition (e.g. publication of DSM-IV); (ii) diagnostic accretion (i.e. children with an initial diagnosis of mental retardation acquiring an autism diagnosis) and expansion (i.e. children with autism at the higher end of the functioning spectrum being diagnosed with greater frequency); (iii) increased awareness of signs and symptoms of autism; and (iv) individual-level risk factors that have increased in frequency (e.g. older parental age at birth). Age-period-cohort analyses of data collected over time can disentangle the time-varying forces shaping trends over time and help to focus investigations of underlying aetiology, as each effect implicates a broad domain of causal factors.

In general, ageperiod- cohort models decompose variance in trends over time into those attributable to age-, periodand cohort-effects. At the individual level, autism diagnosis is strongly related to child age and substantial evidence indicates that the average age of diagnosis has decreased in more recently born cohorts. Age effects (i.e. changes in the age structure of the population or age at diagnostic ascertainment), however, are unlikely to be driving the increased incidence of autism diagnosis as ageadjusted rates still show a marked increase over time. Cohort effects can be conceived of as changes in health status that are confined to or stronger among particular age groups in particular time periods. Cohort effects could arise in these data through a number of potential mechanisms.

For example, if the prevalence of a risk factor acting at conception exhibits change over time (e.g. paternal age), then each successively younger cohort will be differentially exposed, manifesting as a cohort effect (e.g. each successively younger cohort has older fathers). Alternatively, an exposure introduced into the population as a whole could differentially affect autism incidence depending on age of exposure. This would also manifest as a cohort effect. In contrast, if period effects are observed to be the main driver of increased autism diagnosis, then risk factors which have also varied across time but have similar effects across all age groups may be considered as potentially implicated in the increase in autism. Period effects can be conceived of as changes in disease status that affect all age groups simultaneously, and often coincide with widespread environmental or diagnostic nosological changes but may also reflect widespread societal changes in some circumstance. The key in differentiating period from cohort effects is that if period effects are operative, then the incidence of autism should increase across all age groups under study at a particular time, rather than among specific age groups at a particular time.

Evidence to date is strongly suggestive of powerful cohort effects in the incidence and prevalence of ...